Registration of Differently Scaled Images

ABSTRACT

An image registration method involves computing horizontal and vertical integral projection vectors for first and second distorted or partially distorted images. The images are registered by applying a scale factor estimation between the first and second images on the horizontal and vertical integral projection vectors.

RELATED APPLICATIONS

This application is related to a contemporaneously filed application,Registration of Distorted Images, with same inventor and assignee,having docket number FN-370A-US.

BACKGROUND

The invention relates to image registration, and particularly toregistration of distorted, rotated, translated and/or differently scaledimages.

U.S. patent application Ser. No. 12/959,089, filed Dec. 2, 2010, by thesame assignee describes an image acquisition device having a wide fieldof view that includes a lens and image sensor configured to capture anoriginal wide field of view (WFoV) image with a field of view of morethan 90°. The device has an object detection engine that includes one ormore cascades of object classifiers, e.g., face classifiers. A WFoVcorrection engine may apply rectilinear and/or cylindrical projectionsto pixels of the WFoV image, and/or non-linear, rectilinear and/orcylindrical lens elements or lens portions serve to prevent and/orcorrect distortion within the original WFoV image. One or more objectslocated within the original and/or distortion-corrected WFoV imageis/are detectable by the object detection engine upon application of theone or more cascades of object classifiers.

U.S. patent application Ser. No. 13/028,203, filed Feb. 15, 2011, by thesame assignee describes a technique to determine a measure offrame-to-frame rotation for a sequence of two or more images. A globalXY alignment of a pair of frames is performed. Local XY alignments in atleast two matching corner regions of the pair of images are determinedafter the global XY alignment. Based on differences between the local XYalignments, a global rotation is determined between the pair of frames.

U.S. patent application Ser. No. 13/020,805, filed Feb. 3, 2011, by thesame assignee describes an autofocus method that includes acquiringmultiple images each having a camera lens focused at a different focusdistance. A sharpest image is determined among the multiple images.Horizontal, vertical and/or diagonal integral projection (IP) vectorsare computed for each of the multiple images. One or more IP vectors ofthe sharpest image is/are convoluted with multiple filters of differentlengths to generate one or more filtered IP vectors for the sharpestimage. Differences are computed between the one or more filtered IPvectors of the sharpest image and one or more IP vectors of at least oneof the other images of the multiple images. At least one blur width isestimated between the sharpest image and the at least one of the otherimages of the multiple images as a minimum value among the computeddifferences over a selected range. The steps are repeated one or moretimes to obtain a sequence of estimated blur width values. A focusposition is adjusted based on the sequence of estimated blur widthvalues.

U.S. patent application Ser. No. 13/077,891, filed Mar. 31, 2011, by thesame assignee describes a technique of enhancing a scene containing oneor more off-center peripheral regions within an initial distorted imagecaptured with a large field of view. The technique includes determiningand extracting an off-center region of interest (hereinafter “ROI”)within the image. Geometric correction is applied to reconstruct theoff-center ROI into a rectangular or otherwise undistorted or lessdistorted frame of reference as a reconstructed ROI. A quality ofreconstructed pixels is determined within the reconstructed ROI. Imageanalysis is selectively applied to the reconstructed ROI based on thequality of the reconstructed pixels.

U.S. patent application Ser. No. 13/078,970, filed Apr. 2, 2011, by thesame assignee describes a technique of enhancing a scene containing oneor more off-center peripheral regions within an initial distorted imagecaptured with a large field of view. The technique includes determiningand extracting an off-center region of interest (hereinafter “ROI”)within the image. Geometric correction is applied to reconstruct theoff-center ROI into a rectangular or otherwise undistorted or lessdistorted frame of reference as a reconstructed ROI.

U.S. patent application Ser. No. 13/084,340, filed Feb. 15, 2011, by thesame assignee describes a technique of enhancing a scene containing oneor more off-center peripheral regions within an initial distorted imagecaptured with a large field of view. The technique includes determiningand extracting an off-center region of interest (hereinafter “ROI”)within the image. Geometric correction is applied to reconstruct theoff-center ROI into a rectangular or otherwise undistorted or lessdistorted frame of reference as a reconstructed ROI. A quality ofreconstructed pixels is determined within the reconstructed ROI. One ormore additional initially distorted images is/are acquired, and matchingadditional ROIs are extracted and reconstructed to combine with reducedquality pixels of the first reconstructed ROI using a super-resolutiontechnique to provide one or more enhanced ROIs.

Each of these patent applications is hereby incorporated by referenceinto the detailed description as describing alternative embodiments andtechniques that may be used in combination with one or more features ofembodiments described herein.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 a illustrates an original grayscale image.

FIG. 1 b illustrates the grayscale image of FIG. 1 a with application of1.1× zoom.

FIG. 2 shows plots of integral projection vectors of the 1.1× zoomimage, and of six resampled integral projection vectors at various zoomfactors.

FIG. 3 a shows plots of integral projection vectors of the images ofFIGS. 1 a and 1 b obtained by summing their columns.

FIG. 3 b shows plots of integral projection vectors of the image of FIG.1 b, and of resampled integral projection vectors at 1.1× zoom.

FIG. 4 shows a plot of absolute difference between resampled integralprojection vectors of one image and the integral projection vectors of asecond image as a function of the scale (zoom) or resample factor.

FIGS. 5 a, 5 b and 5 c illustrate respectively original lines andpin-cushion and barrel distorted lines.

FIGS. 6 a and 6 b illustrate respectively pin-cushion and barreldistorted images.

FIG. 7 illustrates a technique that determines distorted integralprojection vector for two images with which displacements, rotations,and/or scale factor determinations can be performed on uncorrected,distorted images.

FIG. 8 illustrates an example of application of the technique of FIG. 7and shows plots of an original signal, a distorted signal and arecovered signal that matches the period of the original signal.

FIG. 9 a illustrates examples of horizontal integral projection vectorsfor the images of FIGS. 6 a and 6 b.

FIG. 9 b illustrates examples of distorted horizontal integralprojection vectors for the images of FIGS. 6 a and 6 b.

FIG. 10 a illustrates examples of sums of absolute horizontaldisplacement errors for techniques using undistorted and distortedintegral projection vectors for the pin-cushion distortion case.

FIG. 10 b illustrates examples of sums of absolute vertical displacementerrors for techniques using undistorted and distorted integralprojection vectors for the pin-cushion distortion case.

FIG. 10 c illustrates examples of sums of absolute horizontaldisplacement errors for techniques using undistorted and distortedintegral projection vectors for the barrel distortion case.

FIG. 10 d illustrates examples of sums of absolute vertical displacementerrors for techniques using undistorted and distorted integralprojection vectors for the barrel distortion case.

FIG. 11 is a block diagram that illustrates an image acquisition deviceupon which certain embodiments may be implemented.

FIG. 12 illustrates an example of a general architecture of a stereoimaging system in accordance with certain embodiments.

FIG. 13 illustrates an example internal architecture of a system inaccordance with certain embodiments that includes two conversion blocksrespectively coupled to two camera units.

DETAILED DESCRIPTIONS OF THE EMBODIMENTS

An image registration method is provided that includes acquiring firstand second distorted or partially distorted images of scenes that eachinclude a first object, such as a face. Horizontal and vertical integralprojection vectors are computed for the first and second distorted orpartially distorted images. At least an approximation of an inversedistortion function is applied to the horizontal and vertical integralprojection vectors of the first and second distorted or partiallydistorted images to generate distorted horizontal and vertical integralprojection vectors. The method further includes registering the firstand second distorted or partially distorted images are registered,including applying a displacement, rotation or scale factor estimationbetween the first and second images on the distorted horizontal andvertical integral projection vectors. A further action is performedusing the registered first and second distorted or partially distortedimages.

The registering of the first and second distorted or partially distortedimages may be performed with or without correcting distortion in thefirst and second distorted or partially distorted images.

The computing of the horizontal and vertical integral projection vectorsfor the first and second distorted or partially distorted images mayinclude summing columns and rows of image data, respectively.

A distortion function may be determined for at least a portion of thefirst or the second distorted or partially distorted image, or both. Amodel function may be found that approximately fits the distortion of atleast the portion of the first or second distorted or partiallydistorted image, or both. The distortion function determined for atleast the portion of the first or second distorted or partiallydistorted image, or both, may include: 1+k₁r; or [1+k₁r]⁻¹; or 1+k₁r²;or [1+k₁r²]⁻¹; or 1+k₁r+k₂r²; or (1+k₁r)/(1+k₂r²); or combinationsthereof, any of various other model functions, e.g., as may beunderstood by lens designers and other optical manufacturers andexperts.

The registering may include applying a scale factor estimation thatinvolves:

-   -   resampling at least a portion of the first distorted or        partially distorted image with multiple different resampling        factors over a range of resampling factors to generate multiple        resampled integral projection vectors;    -   computing for each of the different resampling factors an        absolute sum of differences between the resampled integral        projection vectors of the first image and corresponding integral        projection vectors of the second image; and    -   determining the scaling factor between the first and second        distorted or partially distorted images, including finding the        resampling factor that corresponds to the minimum value of the        absolute sums over the range of resampling factors.

Another image registration method includes acquiring first and seconddistorted or partially distorted images of scenes that each include afirst object, e.g., a face. Horizontal and vertical integral projectionvectors are computed for the first and second distorted or partiallydistorted images or distortion-corrected images, or both. The methodfurther includes registering the first and second distorted or partiallydistorted images, or the distortion-corrected images, or both, includingapplying a scale factor estimation between the first and second imageson the horizontal and vertical integral projection vectors. A furtheraction is performed using the registered first and second distorted orpartially distorted images or the corrected images, or both.

The method may include correcting distortion in the first and seconddistorted or partially distorted images prior to computing thehorizontal or vertical integral projection vectors. The computing of thehorizontal and vertical integral projection vectors may involve summingcolumns and rows of image data, respectively.

A distortion function may be determined for at least a portion of thefirst or second distorted or partially distorted image, or both, tocompute the distortion-corrected images or to compute distorted integralprojection vectors, or both. The determining of the distortion functionmay involve finding a model function that approximately fits thedistortion of at least the portion of the first or second distorted orpartially distorted image, or both. The distortion function determinedfor at least the portion of the first or second distorted or partiallydistorted image, or both, may include: 1+k₁r; or [1+k₁r]⁻¹; or 1+k₁r²;or [1+k₁r²]⁻¹; or 1+k₁r+k₂r²; or (1+k₁r)/(1+k₂r²); or combinationsthereof, any of various other model functions, e.g., as may beunderstood by lens designers and other optical manufacturers andexperts.

The applying of the scale factor estimation may include:

-   -   resampling at least a portion of the first distorted or        partially distorted image with multiple different resampling        factors over a range of resampling factors to generate multiple        resampled integral projection vectors;    -   computing for each of the different resampling factors an        absolute sum of differences between the resampled integral        projection vectors of the first image and corresponding integral        projection vectors of the second image; and    -   determining the scaling factor between the first and second        distorted or partially distorted images, including finding the        resampling factor that corresponds to the minimum value of the        absolute sums over the range of resampling factors.

One or more processor-readable storage devices are also provided thathave code embedded therein for programming one or more processors toperform any of the methods described herein. Digital image acquisitiondevices, such as portable cameras and camera-phones, are also providedeach include a lens, an image sensor, a processor and a memory that hascode embedded therein for programming the processor to perform any ofthe methods described herein.

Scale Estimation Method

In certain embodiments, horizontal and vertical integral projectionvectors are computed for a pair of images. The integral projection (IP)vectors of one image are resampled with different resampling factors.The absolute sum of the difference between the resampled IP vectors ofthe one image and the IP vectors of the other image is computed on acommon part, e.g., a central part. The sum of squares may alternativelybe used. The resampling factor that leads to the minimum value over theselected range estimates the scaling factor between images.

In certain embodiments, a scaling factor or zoom factor is estimatedbetween the two images or image crops (grayscale, or other colorplanes). It may be approximated that both images are not blurred andthat there is no rotation nor translation between them, or that theseare otherwise compensated. The horizontal and vertical integralprojection vectors (i.e. the sums of columns and rows respectively) arecomputed for both images. The integral projection vector of one image isresampled with different resampling factors.

In certain embodiments, the resampling changes the sampling rate at aresampling factor multiple, e.g., using a polyphase implementation. Thecommon and/or central part of the resampled vectors is selected. Theabsolute sum of the difference between the resampled IP vectors of oneimage and the IP vector of the other image is computed on the commonand/or central part. The resampling factor that leads to the minimumvalue over the selected range estimates the zoom or scaling factorbetween images. The sum of absolute differences can be replaced by thesum of squared differences.

In certain embodiments, for high accuracy, both horizontal and verticalprojections profiles can be used, particularly when a same scale factoris used on horizontal and vertical directions. In this case, the minimumof the sum of both horizontal and vertical sums of absolute differencesmay be selected to indicate the suitable scaling factor. In otherembodiments, different scaling factors may be used for the horizontaland vertical directions.

In certain embodiments, an extension of a translational displacementmethod may use integral projection methods. In cases of significantrotation and/or radial blur (e.g., in video stabilization or panoramaapplications), a search of the scaling factor can follow after atranslational displacement compensation and/or rotation compensation. Ahigh frequency content change of integral projection vectors from oneframe to another can indicate shaky videos with significant rotations.

In the autofocus case, blur estimation may follow zoom estimation,without applying translational registration. A problem encountered inautofocus systems is that focal changes occur during the acquisition ofthe images. After estimating the zoom between them, the blur radius canbe estimated (see, e.g., U.S. Ser. No. 13/028,206, assigned to the sameassignee and incorporated by reference). In this embodiment, the blurestimation is more exact than in the case without estimating the zoom.

The procedure can be performed on both horizontal and verticalprojection vectors or by splitting both in opposite directions fromtheir centre. The complexity of the method may be greatly reduced,because multiple image re-scaling or complex image transforms like radontransforms would not be involved in these embodiments. Also, thetechnique may be extended to transformed image domains. The estimationof the scaling factor using resampled integral projection vectors of oneimage and their comparison with the integral projection vectors of theother image provides an advantageous and effective image registrationsolution.

Techniques in accordance with certain embodiments involve estimating ascaling factor or zoom factor between two images or image crops(grayscale, or other color planes etc.). In some approximations, it maybe assumed that both images are not blurred and there is no rotation ortranslation between them, or that these factors have been or will beotherwise compensated. The horizontal and vertical integral projectionvectors (see, e.g., K. Sauer and B. Schwartz, 1996, “Efficient BlockMotion Estimation Using Integral Projections”, IEEE Trans. Circuits,Systems for video Tech., vol. 6, No. 5, October, pp. 513-518,incorporated by reference) may be found for both images by computing thesums of all columns and rows respectively. The integral projectionvector of one image is then resampled in certain embodiments withdifferent resampling factors. In some embodiments, the resamplingchanges the sampling rate by multiplying the resampling factor using,for example, a polyphase implementation (see, e.g.,http://www.mathworks.com/help/toolbox/signal/resample.html, incorporatedby reference).

A common part, e.g., a central part, of the resampled vectors isselected. The absolute sum of the difference between the resampled IPvectors of one image and the IP vector of the other image is computed onthis common and/or central part. The resampling factor that leads to theminimum value over the selected range estimates the zoom or scalingfactor between images. The sum of absolute differences can be replacedby the sum of squared differences. Also, for better accuracy, bothhorizontal and vertical projections profiles can be used if identicalscale factor is used on horizontal and vertical directions. In thiscase, the minimum of the sum of both horizontal and vertical sums ofabsolute differences indicates the suitable scaling factor. The proposedmethod can be used in order to find different scaling factors onhorizontal and vertical directions.

FIGS. 1 a-1 b illustrate an example of a pair of grayscale images offsetby a scaling factor or zoom factor of 1.1 to 1. FIG. 2 shows plots ofintegral projection vectors of the 1.1× zoom image, and of six resampledintegral projection vectors at various zoom factors. The resamplingfactors are varied from 0.9 to 1.2 with a 0.05 step. Resampling with afactor 1 is not shown since it doesn't affect the signal and representthe original IP vector (FIG. 3 a). Therefore six (6) different integralprojection vectors (plots of differently colors in FIG. 2) are obtainedand compared with the integral projection vector of the second image(solid black line plot in FIG. 2).

FIG. 3 a shows the integral projection vectors of both images obtainedby summing their columns. FIG. 3 a illustrates that the central part ofthe IP vectors is better matched than the laterals parts. Also,differences increase towards the extremes of the vectors. FIG. 3 b showsthe central zoomed IP vector and the re-sampled original IP vector witha scaling factor 1.1. These represent the best match from FIG. 2.

FIG. 4 confirms that the minimum corresponds to the chosen scalingfactor of 1.1×. In FIG. 4, the sums of absolute differences between theresampled integral projection vectors of one image and the integralprojection vectors of the second image are plotted as a function of thescale/resample factor. The minimum clearly appears at scale factor 1.1.

In certain embodiments, extensions of translational displacement methodsmay be used that employ integral projection methods. Therefore, U.S.patent application Ser. Nos. 12/959,089, filed Dec. 2, 2010, 13/028,203,filed Feb. 15, 2011, 13/020,805, filed Feb. 3, 2011, 13/077,891, filedMar. 31, 2011, 13/078,970, filed Apr. 2, 2011, 13/084,340, filed Feb.15, 2011, which are each assigned to the same assignee as the presentapplication are hereby incorporated by reference.

In cases of significant rotation and/or radial blur, video stabilizationand/or panorama applications may be employed. Therefore, U.S. Pat. Nos.7,639,888, and 7,773,118 and US published applications US20070296833,US20080309769, US20080309770, US20090167893, US20080219581,US20100329582, US20090179999, US20090080796, US20090303343, US20110205381, US20110141227, and US20110141226, which are assigned to thesame assignee as the present application are hereby incorporated byreference.

The search for the scaling factor can follow after the translationaldisplacement compensation and/or rotation compensation. The highfrequency content change of the integral projection vectors from oneframe to another can indicate shaky videos with significant rotations(see U.S. Pat. No. 7,868,922, incorporated by reference).

In applying embodiments in the context of autofocus techniques, blurestimation can follow after zoom estimation, because there is no needfor translational registration. A problem encountered in autofocussystems involves focal changes during the acquisition of images neededfor focusing. In the auto-focus context, U.S. patent application Ser.Nos. 12/959,320, filed Dec. 2, 2010, 12/944, 701, filed Nov. 11, 2010,13/020,805, filed Feb. 3, 2011 and 13/077,936, filed Mar. 21, 2011 areassigned to the same assignee as the present application and are herebyincorporated by reference. After estimating the zoom between them, theblur radius can be estimated using the integral projection-based methoddescribed in U.S. Ser. No. 13/028,205, filed Feb. 15, 2011, which ishereby incorporated by reference. Blur estimation in accordance withthis embodiment is advantageously precise.

The procedure is performed in certain embodiments on both horizontal andvertical projection vectors, and in other embodiments by splitting bothin opposite directions from their centre. The complexity of the methodis somewhat reduced in the latter case, because it doesn't involvemultiple image re-scaling nor complex image transforms like radontransforms (see, e.g., Fawaz Hjouj and David W. Kammler, “Identificationof Reflected, Scaled, Translated, and Rotated Objects from their RadonProjections”, IEEE Trans. On Image Processing, vol. 17, no. 3, March2008, pp. 301-310, incorporated by reference). Also, the process isextendible to transformed image domains.

Distorted Integral Projection Method

A distorted integral projection method for registration of distortedimages is provided and several embodiments are described herein. Afterthe function that models the distortion of the images is found, theapproximation of its inverse is computed. This function is applied tothe integral projection vectors of the distorted images. These computeddistorted integral projection vectors are used for the registration ofthe distorted images.

Images can be distorted in various ways, although common distortionsinvolve pincushion distortions, barrel distortions and fish-eyedistortions. Advantageous integral projection (IP) techniques areapplicable to images having these kinds of distortions. Useful methodsin image registration are provided in certain embodiments betweenimages. Some of these embodiments relate to image pairs havinghorizontal and vertical displacements, rotation, blur size and/orscaling factor differences. Certain embodiments provide approaches toworking with distorted images that utilize integral projectiontechniques that are also applicable to undistorted images, particularlyonce the distorted integral projection vectors are found in as describedherein.

Since the integral projection method sums the pixel values of columns orrows, or both, the integral projection vectors would not be as preciseas desired in applications to distorted images without use of thetechniques described by example in accordance with certain embodiments.The effect of the distortion on the integral projection vectors isadvantageously reduced by applying on them an approximation of theinverse function that models the distortion of the original image.Translation displacements, rotation estimation and/or scale factorestimations between two images are applied to distorted integralprojection vectors to provide advantageously high precision. Betterregistration between corrected images is obtained by using the availabledistorted images. The use of distorted integral projection vectorsimproves the precision of computing the registration parameters withoutthe need to correct the distorted images first.

FIGS. 5 a-5 c illustrate examples of distortion effects on lines. FIG. 5a illustrates an example of an original image. FIG. 5 b illustrates thelines of the original image subjected to a pincushion distortion, whileFIG. 5 c illustrates a barrel distortion. The effect on real images ofthe same pincushion and barrel distortions is illustrated in FIGS. 6 a-6b. These are radial distortions depending on the distance r, and aradial parameter, k.

Examples of such polynomial and rational distortion functions are shownin Table 1.

TABLE 1 Polynomial and rational distortion functions 1 + k₁r$\frac{1}{1 + {k_{1}r}}$ 1 + k₁r² $\frac{1}{1 + {k_{1}r^{2}}}$ 1 +k₁r + k₂r² $\frac{1 + {k_{1}r}}{1 + {k_{2}r^{2}}}$

The effect of the distortion on the integral projection vectors can bereduced by applying on them an approximation of the inverse function(F⁻¹(r)), where F(r) is the function that models the distortion of theoriginal image. The radius r is actually approximated with itsprojection on axis. This approximation would not be valid in cases ofheavy radial distortions (high k coefficients in case of polynomial andrational distortion functions).

FIG. 7 is a block diagram illustrating processes generally involved in atechnique in accordance with these embodiments. The illustrative processof FIG. 7 begins with a pair of distorted images 1 and 2. Integralprojection vectors are computed for these images 1 and 2. One or moreapproximate inverse functions are applied to the computed integralprojection vectors. Now, techniques for translation displacements,rotation estimation and/or scale factor estimations between the twoimages 1 and 2 can be applied on these distorted integral projectionvectors. This method provides advantageous precision with regard tocomputing registration parameters without the need to correct thedistorted images first.

The computation of k coefficient/coefficients may in certain embodimentsbe made by using calibration grids (see, e.g., Athanasios Bismpigiannis,“Measurements and correction of geometric distortion”, Stanford, 2007,http://scien.stanford.edu/pages/labsite/2007/psych221/projects/07/geometric_distortion/geometric_distortion_project.pdf;and Abhishek Das, Dinesh Patil, “Efficient Measurement and CorrectionTechniques for Lens Distortion”, Stanford, 2006,http://scien.stanford.edu/pages/labsite/2006/psych221/projects/06/ddpatil/ee362ProjectReport.pdf;and Harri Ojanen, “Automatic Correction of Lens Distortion by UsingDigital Image Processing”, 1999,http://www.math.rutgers.edu/˜ojanen/undistort/undistort.pdf, which areeach incorporated by reference. Also, commercial software like Imatestcan be used (see, e.g., Imatest software,http://www.imatest.com/docs/distortion.html, which is incorporated byreference. After a good model for the distortion is found, theapproximation of the inverse function is computed analytically or byusing specialized mathematical software like MAPLE or MATLAB. Theinverse function approximation should minimize the error preferably onthe range [1 . . . min{Nl/2, Nc/2}], where Nl and Nc are the number oflines and number of columns of the image, respectively and min is theminimum function (ideally, F(F⁻¹(r))=r).

A 1-D example that shows the principle of the method is illustrated inFIG. 8. A bipolar signal (in blue) is distorted starting from the middleof the signal (in red). The distorted signal has a different periodtowards its ends. After applying the inverse transform on the distortedsignal, the recovered signal has roughly the same period with theoriginal signal. The numerical complexity of the approach from FIG. 7 ismuch smaller, because it doesn't involves certain complex operations onimages.

In FIG. 9 a the horizontal integral projection vectors of two pincushiondistorted “Lena” images are plotted. The original undistorted imageswere translated with 10 pixels. FIG. 9 b shows the horizontal DistortedIntegral Projection (DIP) vectors. The methods are applied on thecentral part of the computed vectors. For the specific case of FIGS. 9a-9 b, the integral projection order method finds a displacement of 9pixels, while the distorted integral projection method finds the exactinitial displacement of 10 pixels.

In FIGS. 10 a-10 d, the horizontal and vertical displacements areindividually varied from 1 to 20 on distorted images (pincushion andbarrel distortions). The absolute sum of errors between the exactdisplacements and the estimated ones using the integral projection anddistorted integral projection methods is computed. The distortedintegral projection method obtains a better approximation of thedisplacement values. As expected, the errors increase in cases of largetranslational values. A good approximation of the inverse of thedistortion is an advantageous component of this technique. Also, bettervalues are obtained by using shorter central parts of the distortedintegral projection vectors, especially in cases of high distortions.Even more precise results are achieved in cases of polynomial andrational distortion functions with linear dependence of the radius. Thelargest geometrical distortion that the method can handle depends on thetype of distortion, image content and the precision of the approximationof the inverse distortion function.

Implementation Mechanisms Hardware Overview

FIG. 11 is a block diagram that illustrates an example of an imageacquisition device 2100 upon which certain embodiments may beimplemented. The image acquisition device 2100 can include an imagecapture apparatus 2120 comprising a lens 2122 and a source of light forproviding illumination during image capture 2124. The image captureapparatus 2120 can further comprise distance analyzer 2128 fordetermining a distance between a point on the device 2100 and a subject.The image capture apparatus 2120 can further comprise a light sensor2126 that can be, for example a CCD, CMOS or any other object thattransforms light information into electronic encoding. The image captureapparatus 2120 can further comprise a mechanism 2129 for monitoringparameters of the lens 2122 during image acquisition. Relevantparameters of the lens during acquisition can include the aperture or anf-stop, which primarily determines the depth of field, the focal lengthwhich determines the enlargement of the image, and the focusing distancewhich determines the distance to the objects at which the lens 2122 wasfocused.

The image acquisition device 2100 may be contained within a singledevice, such as a lens connected to a personal computer, a portablecamera, a smartphone, a video camera with still image capturingcapability, etc. Alternatively, various portions of the imageacquisition device 2100 might be distributed between multiple devices,such as having some components on a personal computer and somecomponents on a portable digital camera. Image acquisition device 2100includes a bus 2102 or other communication mechanism for communicatinginformation, and a processor 2104 coupled with bus 2102 for processinginformation. Image acquisition device 2100 also includes a main memory2106, such as a random access memory (“RAM”) or other dynamic storagedevice, coupled to bus 2102 for storing information and instructions tobe executed by processor 2104. Main memory 2106 also may be used forstoring temporary variables or other intermediate information duringexecution of instructions to be executed by processor 2104. Imageacquisition device 2100 further includes a read only memory (“ROM”) 2108or other static storage device coupled to bus 2102 for storing staticinformation and instructions for processor 2104. A storage device 2110,such as a magnetic disk or optical disk, is provided and coupled to bus2102 for storing information and instructions.

Image acquisition device 2100 may be coupled via bus 2102 to a display2112, such as a liquid crystal display (LCD), for displaying informationor images to a user. An input device 2114, including keys, is coupled tobus 2102 for communicating information and command selections toprocessor 2104. Other types of user input devices, for example cursorcontrollers 2116 such as a mouse, trackball, stylus, or cursor directionkeys for communicating direction information and command selections toprocessor 2104 can also be used for controlling cursor movement ondisplay 2112 for communicating information and command selections toprocessor 2104.

The term “computer-readable medium” as used herein refers to any mediumthat participates in providing instructions to processor 2104 forexecution. Such a medium may take many forms, including but not limitedto non-volatile media and volatile media. Non-volatile media includes,for example, optical or magnetic disks, such as storage device 2110.Volatile media includes dynamic memory, such as main memory 2106.

Common forms of computer-readable media include, for example, a floppydisk, a flexible disk, hard disk, magnetic tape, or any other magneticmedium, a CD-ROM, any other optical medium, punchcards, papertape, anyother physical medium with patterns of holes, a RAM, a PROM, and EPROM,a FLASH-EPROM, any other memory chip or cartridge, a carrier wave asdescribed hereinafter, or any other medium from which a computer canread. Any single type or combination of computer-readable media can beused for storing instructions that, when executed by one or moreprocessors, cause the processors to carry out steps corresponding to thetechniques of the present invention.

Hardware Architecture of Stereo Imaging System

An example of a general architecture of a stereo imaging system isillustrated at FIG. 12, which shows two CMOS sensors and a VGA monitorconnected to a power PC with Xilinx Virtex4 FPGA and DDR SDRAM. The twoCMOS sensors are connected to an FPGA which incorporates a PowerPC coreand associated SDRAM. Additional system components can be added toimplement a dual stereo image processing pipeline (see, e.g., I. Andorkoand P. Corcoran, “FPGA Based Stereo Imaging System with Applications inComputer Gaming”, at International IEEE Consumer Electronics Society'sGames Innovations Conference 2009 (ICE-GIC 09), London, UK, incorporatedby reference).

The development board is a Xilinx ML405 development board, with a Virtex4 FPGA, a 64 MB DDR SDRAM memory, and a PowerPC RISC processor. Theclock frequency of the system is 100 MHz. An example internalarchitecture of the system in accordance with certain embodiments isillustrated at FIG. 13 which shows two conversion blocks respectivelycoupled to camera units 1 and 2. The camera units 1 and 2 feed a PLBthat feeds a VGA controller. A DCR is connected to the camera units 1and 2, the VGA controller, an I2C controller and a Power PC. The PLB isalso coupled with DDR SDRAM. The sensor used in this embodiment includesa ⅓ inch SXGA CMOS sensor made by Micron. It has an active zone of1280×1024 pixels. It is programmable through the I2C interface. It worksat 13.9 fps and the clock frequency is 25 MHz. This sensor was selectedbecause of its small size, low cost and the specifications of thesesensors are satisfactory for this project. This system enables real-timestereo video capture with a fixed distance between the two imagingsensors.

While an exemplary drawings and specific embodiments of the presentinvention have been described and illustrated, it is to be understoodthat that the scope of the present invention is not to be limited to theparticular embodiments discussed. Thus, the embodiments shall beregarded as illustrative rather than restrictive, and it should beunderstood that variations may be made in those embodiments by workersskilled in the arts without departing from the scope of the presentinvention.

In addition, in methods that may be performed according to preferredembodiments herein and that may have been described above, theoperations have been described in selected typographical sequences.However, the sequences have been selected and so ordered fortypographical convenience and are not intended to imply any particularorder for performing the operations, except for those where a particularorder may be expressly set forth or where those of ordinary skill in theart may deem a particular order to be necessary.

In addition, all references cited above and below herein, as well as thebackground, invention summary, abstract and brief description of thedrawings, are all incorporated by reference into the detaileddescription of the preferred embodiments as disclosing alternativeembodiments. In addition, features described at any of U.S. Pat. Nos.7,660,478, 7,639,889, 7,636,486, 7,639,888, 7,697,778, 7,773,118,7,676,108, 7,362,368, 7,692,696, 7,317,815, 7,676,108, 7,687,778,7,606,417, 7,680,342, 7,796,822, 7,634,109, 7,787,022, 7,474,341,7,352,394, 7,636,486, and 6,407,777, and/or United States publishedpatent applications nos. 2008/0143854, 2010/0039525, 2009/0303342,2009/0303343, 2009/0273685, 2009/0196466, 2008/0316328, 2009/0179998,2009/0115915, 2009/0080713, 2009/0003708, 2009/0003661, 2009/0002514,2008/0317379, 2008/0143854, 2008/0043121, 2011/0013044, 2011/0002545,2009/0167893, 2008/0309769, 2008/0219581, 2009/0179999, 2007/0269108,2008/0219581, 2008/0309770 and 2007/0296833, and U.S. patent applicationSer. Nos. 12/944,701, 12/944,703, 12/901,577, 12/820,002, 12/820,034,12/820,086, and 12/959,320, which are hereby incorporated by reference,may be used in alternative embodiments.

What is claimed is:
 1. An image registration method, comprising:acquiring first and second distorted or partially distorted images ofscenes that each include a first object; computing horizontal andvertical integral projection vectors for the first and second distortedor partially distorted images or distortion-corrected images, or both;registering the first and second distorted or partially distortedimages, or the distortion-corrected images, or both, including applyinga scale factor estimation between the first and second images on thehorizontal and vertical integral projection vectors; and performing afurther action using the registered first and second distorted orpartially distorted images or the corrected images, or both.
 2. Themethod of claim 1, further comprising correcting distortion in the firstand second distorted or partially distorted images prior to computingthe horizontal or vertical integral projection vectors.
 3. The method ofclaim 1, wherein the computing of the horizontal and vertical integralprojection vectors comprises summing columns and rows of image data,respectively.
 4. The method of claim 1, further comprising determining adistortion function for at least a portion of the first or seconddistorted or partially distorted image, or both, to compute saiddistortion-corrected images or to compute distorted integral projectionvectors, or both.
 5. The method of claim 4, wherein the determining adistortion function comprises finding a model function thatapproximately fits the distortion of at least said portion of the firstor second distorted or partially distorted image, or both.
 6. The methodof claim 5, wherein the distortion function determined for at least saidportion of the first or second distorted or partially distorted image,or both, comprises: 1+k₁r; or [1+k₁r]⁻¹; or 1+k₁r²; or [1+k₁r²]⁻¹; or1+k₁r+k₂r²; or (1+k₁r)/(1+k₂r²); or combinations thereof.
 7. The methodof claim 1, wherein the applying a scale factor estimation comprises:resampling at least a portion of the first distorted or partiallydistorted image with multiple different resampling factors over a rangeof resampling factors to generate multiple resampled integral projectionvectors; computing for each of the different resampling factors anabsolute sum of differences, or sum of squares, between the resampledintegral projection vectors of the first image and correspondingintegral projection vectors of the second image; and determining thescaling factor between the first and second distorted or partiallydistorted images, including finding the resampling factor thatcorresponds to the minimum value of the absolute sums over the range ofresampling factors.
 8. The method of claim 1, wherein the first objectcomprises a face.
 9. One or more non-transitory processor-readable mediahaving code embedded therein for programming one or more processors toperform an image registration method, wherein the method comprises:acquiring first and second distorted or partially distorted images ofscenes that each include a first object; computing horizontal andvertical integral projection vectors for the first and second distortedor partially distorted images or distortion-corrected images, or both;registering the first and second distorted or partially distortedimages, or the distortion-corrected images, or both, including applyinga scale factor estimation between the first and second images on thehorizontal and vertical integral projection vectors; and performing afurther action using the registered first and second distorted orpartially distorted images or the corrected images, or both.
 10. The oneor more non-transitory processor-readable media of claim 9, wherein themethod further comprises correcting distortion in the first and seconddistorted or partially distorted images prior to computing thehorizontal or vertical integral projection vectors.
 11. The one or morenon-transitory processor-readable media of claim 9, wherein thecomputing of the horizontal and vertical integral projection vectorscomprises summing columns and rows of image data, respectively.
 12. Theone or more non-transitory processor-readable media of claim 9, whereinthe method further comprises determining a distortion function for atleast a portion of the first or second distorted or partially distortedimage, or both, to compute said distortion-corrected images or tocompute distorted integral projection vectors, or both.
 13. The one ormore non-transitory processor-readable media of claim 12, wherein thedetermining a distortion function comprises finding a model functionthat approximately fits the distortion of at least said portion of thefirst or second distorted or partially distorted image, or both.
 14. Theone or more non-transitory processor-readable media of claim 13, whereinthe distortion function determined for at least said portion of thefirst or second distorted or partially distorted image, or both,comprises: 1+k₁r; or [1+k₁r]⁻¹; or 1+k₁r²; or [1+k₁r²]⁻¹; or 1+k₁r+k₂r²;or (1+k₁r)/(1+k₂r²); or combinations thereof.
 15. The one or morenon-transitory processor-readable media of claim 9, wherein the applyinga scale factor estimation comprises: resampling at least a portion ofthe first distorted or partially distorted image with multiple differentresampling factors over a range of resampling factors to generatemultiple resampled integral projection vectors; computing for each ofthe different resampling factors an absolute sum of differences, or sumof squares, between the resampled integral projection vectors of thefirst image and corresponding integral projection vectors of the secondimage; and determining the scaling factor between the first and seconddistorted or partially distorted images, including finding theresampling factor that corresponds to the minimum value of the absolutesums over the range of resampling factors.
 16. The one or morenon-transitory processor-readable media of claim 9, wherein the firstobject comprises a face.
 17. A digital image acquisition device,comprising: a lens, an image sensor, a processor, a memory having codeembedded therein for programming the processor to perform an imageregistration method, wherein the method comprises: acquiring first andsecond distorted or partially distorted images of scenes that eachinclude a first object; computing horizontal and vertical integralprojection vectors for the first and second distorted or partiallydistorted images or distortion-corrected images, or both; registeringthe first and second distorted or partially distorted images, or thedistortion-corrected images, or both, including applying a scale factorestimation between the first and second images on the horizontal andvertical integral projection vectors; and performing a further actionusing the registered first and second distorted or partially distortedimages or the corrected images, or both.
 18. The device of claim 17,wherein the method further comprises correcting distortion in the firstand second distorted or partially distorted images prior to computingthe horizontal or vertical integral projection vectors.
 19. The deviceof claim 17, wherein the computing of the horizontal and verticalintegral projection vectors comprises summing columns and rows of imagedata, respectively.
 20. The device of claim 17, wherein the methodfurther comprises determining a distortion function for at least aportion of the first or second distorted or partially distorted image,or both, to compute said distortion-corrected images or to computedistorted integral projection vectors, or both.
 21. The device of claim20, wherein the determining a distortion function comprises finding amodel function that approximately fits the distortion of at least saidportion of the first or second distorted or partially distorted image,or both.
 22. The device of claim 21, wherein the distortion functiondetermined for at least said portion of the first or second distorted orpartially distorted image, or both, comprises: 1+k₁r; or [1+k₁r]⁻¹; or1+k₁r²; or [1+k₁r²]⁻¹; or 1+k₁r+k₂r²; or (1+k₁r)/(1+k₂r²); orcombinations thereof.
 23. The device of claim 17, wherein the applying ascale factor estimation comprises: resampling at least a portion of thefirst distorted or partially distorted image with multiple differentresampling factors over a range of resampling factors to generatemultiple resampled integral projection vectors; computing for each ofthe different resampling factors an absolute sum of differences, or sumof squares, between the resampled integral projection vectors of thefirst image and corresponding integral projection vectors of the secondimage; and determining the scaling factor between the first and seconddistorted or partially distorted images, including finding theresampling factor that corresponds to the minimum value of the absolutesums over the range of resampling factors.
 24. The device of claim 17,wherein the first object comprises a face.